Energy efficient strategies for object tracking in sensor networks: A data mining approach

S. Tseng*, Kawuu W. Lin

*Corresponding author for this work

Research output: Contribution to journalArticle

27 Scopus citations

Abstract

In recent years, a number of studies have been done on object tracking sensor networks (OTSNs) due to the wide applications. One important research issue in OTSNs is the energy saving strategy in considering the limited power of sensor nodes. The past studies on energy saving in OTSNs considered the object's movement behavior as randomness. In some real applications, however, the object movement behavior is often based on certain underlying events instead of randomness completely. In this paper, we propose a novel data mining algorithm named TMP-Mine with a special data structure named TMP-Tree for efficiently discovering the temporal movement patterns of objects in sensor networks. To our best knowledge, this is the first work on mining the movement patterns associated with time intervals in OTSNs. Moreover, we propose novel location prediction strategies that utilize the discovered temporal movement patterns so as to reduce the prediction errors for energy savings. Through empirical evaluation on various simulation conditions and real dataset, TMP-Mine and the proposed prediction strategies are shown to deliver excellent performance in terms of scalability, accuracy and energy efficiency.

Original languageEnglish
Pages (from-to)1678-1698
Number of pages21
JournalJournal of Systems and Software
Volume80
Issue number10
DOIs
StatePublished - 1 Oct 2007

Keywords

  • Data mining
  • Location prediction
  • Object tracking
  • Sensor networks
  • Temporal movement patterns

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